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Study: Connecticut leads in Twitter TSA complaints

Connecticut is the leading state for tweets complaining about the Transportation Security Administration, according to a study conducted by the website Travelmath.com.

The study, which analyzed tweets mentioning the TSA from January to April, revealed that the biggest number of negative tweets about the agency were sent from locations in Connecticut, Nevada, Utah, Pennsylvania and Missouri.

By contrast, the report said Kentucky, Tennessee, Wisconsin, Virginia and Alabama lead in positive tweets about the TSA.

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Airports in Oakland; Long Beach, Calif.; Charlotte, N.C.; San Jose, Calif.; and San Diego led for negative TSA tweets, while airports in Burbank, Calif.; New Orleans, Austin, Texas; Denver and Portland, Ore., led for positive ones.

The authors of the study said the analysis of tweets about TSA reveals the agency still faces challenges with winning over airline passengers after nearly 13 years of existence.

"Frustrations at the airport are nothing new to any seasoned traveler. Still, there are many variables that can impact customer satisfaction," the study said.

"What isn’t clear is how much of an impact other variables have on a passenger’s perception of the TSA," the study said. "Could a better overall flying experience reduce the negative sentiment toward the TSA? We may never know, as U.S. airports are markedly absent from a recent report highlighting the 10 best airports in the world."

Travelmath acknowledged passengers were likely more inclined to take to Twitter to vent about the TSA if they had a bad experience, but the group said the agency could still learn lessons from its data.

"If someone feels slighted by the TSA, there is a great chance they will make it known to the world," the group said. "And with such instances so heavily skewed toward the negative, one can’t help but believe there is significant room for improvement. High levels of security and a positive customer experience are not mutually exclusive, but it may be up to the TSA to optimize a blend of these two factors."